The neural renderer (Figure REF  middle) takes the multi-resolution neural image to produce a single HDR output image.
It consists of a four layer fully convolutional U-Net with skip-connections, where the lower resolution input images are concatenated to the intermediate feature tensors.
Downsampling is performed using average-pooling and the images are upsampled by bilinear interpolation.
As convolution primitives, we use gated convolutions [1]} which have been initially developed for hole-filling tasks and are therefore well suited for sparse point input.
Overall the network architecture is similar to Aliev et al. [2]} with one less layer and a few modifications to enable HDR imaging.
First, we remove the batch-normalization layers, as they normalize the mean and standard deviation of intermediate images to fixed values.
The total sensor irradiance is therefore lost and cannot be propagated from a 3D point to the final image.
Additionally, we store the neural point descriptors logarithmically if the scene's radiance range is sizable (larger than \(1:400\) ).
Otherwise the neural descriptors are stored linearly.
For logarithmic descriptors, we convert them to linear space during rasterization so that the convolution operations only use linear brightness values.
